import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

__all__ = ['ResNet', 'resnet10',
           'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']

model_urls = {
  'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
  'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
  'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
  'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
  'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

default_pytorch_bn_momentum = 0.001


def conv3x3(in_planes, out_planes, stride=1):
  "3x3 convolution with padding"
  return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                   padding=1, bias=False)


class BasicBlock(nn.Module):
  expansion = 1

  def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=default_pytorch_bn_momentum):
    super(BasicBlock, self).__init__()
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    residual = x

    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)

    out = self.conv2(out)
    out = self.bn2(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out


class Bottleneck(nn.Module):
  expansion = 4

  def __init__(self, inplanes, planes, stride=1, downsample=None,
               bn_momentum=default_pytorch_bn_momentum):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                           padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
    self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(planes * 4, momentum=bn_momentum)
    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    residual = x

    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)

    out = self.conv2(out)
    out = self.bn2(out)
    out = self.relu(out)

    out = self.conv3(out)
    out = self.bn3(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out


class ResNet(nn.Module):
  def __init__(self, block, layers, num_classes=None,
               bn_momentum=default_pytorch_bn_momentum, dropout_rate=0.5):
    self.bn_momentum = bn_momentum
    self.inplanes = 64
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64, momentum=self.bn_momentum)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7)
    self.dropout = nn.Dropout(p=dropout_rate)

    self.num_classes = num_classes
    if num_classes is not None:
      self.fc = nn.Linear(512 * block.expansion, num_classes)
      # Initialize fc layer with gaussian weights and zero bias.
      self.fc.weight.data.normal_(0, 0.001)
      self.fc.bias.data.zero_()

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()

  def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion, momentum=self.bn_momentum),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample, bn_momentum=self.bn_momentum))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes, bn_momentum=self.bn_momentum))

    return nn.Sequential(*layers)

  def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)

    x = self.layer1(x)
    x = self.layer2(x)
    x = self.layer3(x)
    x = self.layer4(x)

    x = self.avgpool(x)
    feats = x.view(x.size(0), -1)
    feats = self.dropout(feats)

    if self.num_classes is not None:
      logits = self.fc(feats)
      return feats, logits
    else:
      return feats


class ResNetBottom(nn.Module):
  """The bottom layers of resnet."""

  def __init__(self, layers=(3, 4, 6, 3), block=Bottleneck, bn_momentum=default_pytorch_bn_momentum, dropout_rate=None):
    self.bn_momentum = bn_momentum
    self.inplanes = 64
    super(ResNetBottom, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64, momentum=self.bn_momentum)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    # self.avgpool = nn.AvgPool2d(7)

    self.dropout = None
    if dropout_rate is not None:
      self.dropout = nn.Dropout(p=dropout_rate)

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()

  def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion, momentum=self.bn_momentum),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample, bn_momentum=self.bn_momentum))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes, bn_momentum=self.bn_momentum))

    return nn.Sequential(*layers)

  def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)

    x = self.layer1(x)
    x = self.layer2(x)
    x = self.layer3(x)
    # x = self.layer4(x)
    #
    # x = self.avgpool(x)
    # x = self.dropout(x)
    # feats = x.view(x.size(0), -1)

    if self.dropout is not None:
      x = self.dropout(x)

    return x


class ResNetTop(nn.Module):
  def __init__(self, block=Bottleneck, layers=(3, 4, 6, 3), num_classes=None,
               bn_momentum=default_pytorch_bn_momentum, dropout_rate=0.5,
               return_last_conv=False):
    super(ResNetTop, self).__init__()
    self.bn_momentum = bn_momentum
    # This should be modified accordingly.
    self.inplanes = 1024
    # self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
    #                        bias=False)
    # self.bn1 = nn.BatchNorm2d(64, momentum=self.bn_momentum)
    # self.relu = nn.ReLU(inplace=True)
    # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    # self.layer1 = self._make_layer(block, 64, layers[0])
    # self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    # self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7)
    self.dropout = nn.Dropout(p=dropout_rate)

    self.num_classes = num_classes
    if num_classes is not None:
      self.fc = nn.Linear(512 * block.expansion, num_classes)
      # Initialize fc layer with gaussian weights and zero bias.
      self.fc.weight.data.normal_(0, 0.001)
      self.fc.bias.data.zero_()

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
    # Whether to return the activation right before avg_pool layer during
    # forwarding.
    self.return_last_conv = return_last_conv

  def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion, momentum=self.bn_momentum),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample,
                        bn_momentum=self.bn_momentum))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes, bn_momentum=self.bn_momentum))

    return nn.Sequential(*layers)

  def forward(self, x):
    # x = self.conv1(x)
    # x = self.bn1(x)
    # x = self.relu(x)
    # x = self.maxpool(x)
    #
    # x = self.layer1(x)
    # x = self.layer2(x)
    # x = self.layer3(x)
    last_conv = self.layer4(x)
    x = self.avgpool(last_conv)
    feats = x.view(x.size(0), -1)
    feats = self.dropout(feats)

    if self.num_classes is not None:
      logits = self.fc(feats)
      if self.return_last_conv:
        return feats, logits, last_conv
      else:
        return feats, logits
    else:
      return feats


def get_fs_ft_params(resnet_model):
  """Return the 'ft' and 'fs' parameters of a resnet model. Both of them are 
  sorted by names.
  Notations:
    'fs': Parameters trained from scratch, that's fc layer here
    'ft': Fine-tuned parameters.
  Why this function:
    The saving and loading mechanism of torch.optim.Optimizer requires that
    the parameters in the ckpt and in the current model should have same order!
  """
  named_param_fs = list(resnet_model.fc.named_parameters(prefix='fc'))
  named_param_ft = list(
    set(resnet_model.named_parameters()) - set(named_param_fs))
  named_param_fs.sort(key=(lambda tup: tup[0]))
  named_param_ft.sort(key=(lambda tup: tup[0]))
  param_fs = [param for name, param in named_param_fs]
  param_ft = [param for name, param in named_param_ft]

  return param_ft, param_fs


def load_state_dict(model, state_dict):
  """Copies parameters and buffers from `state_dict` into `model` and its 
  descendants. The keys of `state_dict` NEED NOT exactly match the keys 
  returned by model's `state_dict()` function. For dict key mismatch, just
  skip it; for copying error, just output warnings and proceed.

  Arguments:
    model: A torch.nn.Module object. 
    state_dict (dict): A dict containing parameters and persistent buffers.
  Note:
    This is copied and modified from torch.nn.modules.module.load_state_dict().
    Just to allow name mismatch between `model.state_dict()` and `state_dict`.
  """
  import warnings
  from torch.nn import Parameter

  own_state = model.state_dict()
  for name, param in state_dict.items():
    if name not in own_state:
      warnings.warn('Skipping unexpected key "{}" in state_dict'.format(name))
      continue
    if isinstance(param, Parameter):
      # backwards compatibility for serialized parameters
      param = param.data
    try:
      own_state[name].copy_(param)
    except Exception, msg:
      warnings.warn("Error occurs when copying from state_dict['{}']: {}"
                    .format(name, str(msg)))

  missing = set(own_state.keys()) - set(state_dict.keys())
  if len(missing) > 0:
    warnings.warn(
      "Keys not found in state_dict and thus not overwritten: '{}'"
        .format(missing))


def resnet_bottom(pretrained=False, **kwargs):
  """Constructs a ResNetBottom model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNetBottom(**kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
  return model


def resnet_top(pretrained=False, **kwargs):
  """Constructs a ResNetTop model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNetTop(**kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
  return model


def resnet10(**kwargs):
  """Constructs a ResNet-10 model.
  """
  model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
  return model


def resnet18(pretrained=False, **kwargs):
  """Constructs a ResNet-18 model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet18']))
  return model


def resnet34(pretrained=False, **kwargs):
  """Constructs a ResNet-34 model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet34']))
  return model


def resnet50(pretrained=False, **kwargs):
  """Constructs a ResNet-50 model.

  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  Note:
    The resulting behavior of `pretrained=True` in this function is modified.
  """
  model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
  return model


def resnet101(pretrained=False, **kwargs):
  """Constructs a ResNet-101 model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet101']))
  return model


def resnet152(pretrained=False, **kwargs):
  """Constructs a ResNet-152 model.

  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
  if pretrained:
    load_state_dict(model, model_zoo.load_url(model_urls['resnet152']))
  return model
